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Luis Ibarra

Monterrey Institute of Technology and Higher EducationMexico

Luis M. Ibarra received the BS degree in mechatronics engineering from Tecnológico de Monterrey, Mexico City, Mexico, in 2011 and the PhD degree in intelligent control from the same institution in 2016. He is currently working as a postdoc in the \Laboratorio Binacional\ project headed by the Mexican Energy Secretariat (SENER) and the Mexican Science and Technology Council (CONACYT) with special focus on power electronics control. From 2012 to 2015, he was a research assistant and lecturer with the Tecnológico de Monterrey, Mexico City, Mexico. His research interest includes robust and intelligent control techniques, power electronics, and renewable energies. Most of his work is centered in fuzzy logic control, robust control, and electric machinery.

1chapters authored

Chapters authored

Commonly, complex and uncertain plants cannot be faced through well-known linear approaches. Most of the time, complex controllers are needed to attain expected stability and robustness; however, they usually lack a simple design methodology and their actual implementation is difficult (if not impossible). Fuzzy logic control is an intelligent technique which, on its basis, allows the translation from logic statements to a nonlinear mapping. Although it has been proven to effectively deal with complex plants, many recent studies have moved away from the basic premise of linguistic interpretability. In this work, a simple fuzzy controller is designed in a clear way, privileging design easiness and logical consistency of linguistic operators. It is simulated together to a nonlinear model of a quadcopter with added actuators variability, so the robust operation of the controller is also proven. Uneven gain, bandwidth, and time-delay variations are applied among quadcopter’s motors, so the simulations results enclose those characteristics which could be found in reality. As those variations can be related to actuators’ performance, an analysis can be driven in terms of the features which are not commonly included in mathematical models like power electronics drives or electric machinery. These considerations may shorten the gap between simulation and actual implementation of the fuzzy controller. Briefly, this chapter presents a simple fuzzy controller which deals with a quadcopter plant as a first approach to intelligent control.